一種適用于小樣本的迭代多重信號(hào)分類(lèi)算法
doi: 10.11999/JEIT190160
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西安電子科技大學(xué)雷達(dá)信號(hào)處理國(guó)家重點(diǎn)實(shí)驗(yàn)室 西安 710071
Iterative Multiple Signal Classification Algorithm with Small Sample Size
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National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
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摘要:
當(dāng)樣本數(shù)不足時(shí)，由采樣協(xié)方差矩陣特征分解得到的噪聲子空間偏離其真實(shí)值，使得多重信號(hào)分類(lèi)(MUSIC)算法目標(biāo)角度(DOA)估計(jì)性能下降。為了解決這個(gè)問(wèn)題，該文提出了一種迭代算法通過(guò)校正信號(hào)子空間來(lái)提高M(jìn)USIC算法性能。該方法首先利用采樣協(xié)方差矩陣特征分解得到的噪聲子空間粗略估計(jì)目標(biāo)角度；其次基于信源的稀疏性和導(dǎo)向矢量的低秩特性，由上一步得到的目標(biāo)角度以及其鄰域角度對(duì)應(yīng)的導(dǎo)向矢量構(gòu)造一個(gè)新的信號(hào)子空間；最后通過(guò)解一個(gè)優(yōu)化問(wèn)題來(lái)校正信號(hào)子空間。仿真結(jié)果表明，該算法有效地提高了子空間估計(jì)精度?；谛碌男盘?hào)子空間實(shí)現(xiàn)MUSIC DOA估計(jì)可以使得性能得到改善，且在低樣本數(shù)下改善尤為明顯。
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關(guān)鍵詞:
- 目標(biāo)角度估計(jì) /
- 多重信號(hào)分類(lèi)算法 /
- 迭代 /
- 優(yōu)化問(wèn)題
Abstract:For cases with small samples, the estimated noise subspace obtained from sample covariance matrix deviates from the true one, which results in MUltiple SIgnal Classification (MUSIC) Direction-Of-Arrival (DOA) estimation performance breakdown. To deal with this problem, an iterative algorithm is proposed to improve the MUSIC performance by modifying the signal subspace in this paper. Firstly, the DOAs are roughly estimated based on the noise subspace obtained from sample covariance matrix. Then, considering the sparsity of signals and the low-rank property of steering matrix, a new signal subspace is got from the steering matrix consisting of estimated DOAs and their adjacent angles. Finally, the signal subspace is modified by solving an optimization problem. Simulation results demonstrate the proposed algorithm can improve the subspace estimation accuracy and furtherly improve the MUSIC DOA estimation performance, especially in small sample cases.
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